Mining customer preference ratings for product
recommendation using the support vector machine and the
latent class model
William K. Cheung, James T. Kwok, Martin H. Law and Kwok-Ching Tsui
Abstract:
As Internet commerce becomes more popular, customers' preferences on various
products
can now be readily acquired on-line via various
e-commerce systems. Properly mining this extracted data can generate useful knowledge
for providing personalized product recommendation services.
In general, recommender systems use two complementary techniques.
Content-based systems match customer interests with products attributes,
while collaborative filtering systems
utilize preference ratings from other customers.
In this paper, we address some problems faced by these two systems, and
study how machine learning techniques, namely the support vector machine and the
latent class model, can be used to alleviated them.
Proceedings of the Second International Conference on Data Mining Methods
and Databases for Engineering,
pp.601-610, Cambridge, UK, July 2000.
Postscript:
http://www.cs.ust.hk/~jamesk/papers/dm00.ps.gz
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